UGC CARE norms ugc approved journal norms IJRTI Research Journal

Click Here

International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

Call For Paper

For Authors

Forms / Download

Published Issue Details

Editorial Board

Other IMP Links

Facts & Figure

Impact Factor : 8.14

Issue per Year : 12

Volume Published : 8

Issue Published : 82

Article Submitted : 6292

Article Published : 3403

Total Authors : 8672

Total Reviewer : 545

Total Countries : 74

Indexing Partner


This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Published Paper Details
Paper Title: Precision Crop Care Recommender System Using Machine Learning Techniques.
Authors Name: Pratham Kapratwar , Dr. T. Praveen Blessington , Ishwar Mahajan , Pallavi Dandge , Trupti Ghogare
Download E-Certificate: Download
Author Reg. ID:
Published Paper Id: IJRTI2303011
Published In: Volume 8 Issue 3, March-2023
Abstract: Agriculture is India's main source of employment and revenue. Choosing the incorrect crop for their land and using the incorrect fertilizer are the two biggest problems Indian farmers face. Their output will thus considerably decrease. With the help of the Precision Crop Care Recommender System, the farmer’s problem has been solved. The Precision Crop Care Recommender System is a modern farming method that suggests the best crop to farmers as well as fertiliser suggestions based on site-specific attributes using research data on soil properties, soil types, and crop production statistics. This increases output and decreases the frequency of incorrect crop selection. A recommendation system using ML models and a majority vote technique is developed in order to accurately and effectively recommend a crop for the site-specific factors. It employs Logistic Regression, Support Vector Machine (SVM), Random Forest, and Decision Tree. Python logic serves as the only foundation for the fertiliser recommendation system. After classifying the most variable nutrient t as HIGH or LOW, recommendations are then retrieved in line with the findings.
Keywords: Agriculture, Recommendation system, Random Forest, Support Vector Machine (SVM), Logistic Regression.
Cite Article: "Precision Crop Care Recommender System Using Machine Learning Techniques.", International Journal of Science & Engineering Development Research (, ISSN:2455-2631, Vol.8, Issue 3, page no.61 - 64, March-2023, Available :
Downloads: 00034
ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2303011
Registration ID:185443
Published In: Volume 8 Issue 3, March-2023
DOI (Digital Object Identifier):
Page No: 61 - 64
Country: Pune, Maharashtra, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL :
Published Paper PDF:
Share Article:

Click Here to Download This Article

Article Preview
Click Here to Download This Article

Major Indexing from
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

ISSN Details

ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016

DOI (A digital object identifier)

Providing A digital object identifier by DOI.ONE
How to Get DOI?


Open Access License Policy

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Creative Commons License This material is Open Knowledge This material is Open Data This material is Open Content

Important Details

Social Media

Join RMS/Earn 300


Indexing Partner